+
    œ°³j”  ã                   ó„   € R t ^ RIHt ^RIHt ^RIHt ^RIHt ^RI	H
t
 ]! RR7      ] ! R R	]4      4       4       tR	.tR
# )zUperNet model configuration)Ústrict)Ú%consolidate_backbone_kwargs_to_config)ÚPreTrainedConfig)Úauto_docstring)Ú
AutoConfigzopenmmlab/upernet-convnext-tiny)Ú
checkpointc                   óz   a a€ ] tR t^t oRtRtR]/tRtRt	Rt
RtRtRtRtR	t^tR
t^ÿtV 3R ltV3R ltRtVtV ;t# )ÚUperNetConfigao  
pool_scales (`tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
    Pooling scales used in Pooling Pyramid Module applied on the last feature map.
use_auxiliary_head (`bool`, *optional*, defaults to `True`):
    Whether to use an auxiliary head during training.
auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
    Weight of the cross-entropy loss of the auxiliary head.
auxiliary_in_channels (`int`, *optional*, defaults to 256):
    Number of input channels in the auxiliary head.
auxiliary_channels (`int`, *optional*, defaults to 256):
    Number of channels to use in the auxiliary head.
auxiliary_num_convs (`int`, *optional*, defaults to 1):
    Number of convolutional layers to use in the auxiliary head.
auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
    Whether to concatenate the output of the auxiliary head with the input before the classification layer.
loss_ignore_index (`int`, *optional*, defaults to 255):
    The index that is ignored by the loss function.

Examples:

```python
>>> from transformers import UperNetConfig, UperNetForSemanticSegmentation

>>> # Initializing a configuration
>>> configuration = UperNetConfig()

>>> # Initializing a model (with random weights) from the configuration
>>> model = UperNetForSemanticSegmentation(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```ÚupernetÚbackbone_configNi   g{®Gáz”?Tgš™™™™™Ù?é   Fc                óv   <€ \        RR V P                  RRRR. RO//VB w  V n        p\        SV `  ! R/ VB  R# )r   Údefault_config_typeÚresnetÚdefault_config_kwargsÚout_featuresN© )Ústage1Ústage2Ústage3Ústage4)r   r   ÚsuperÚ__post_init__)ÚselfÚkwargsÚ	__class__s   &,€Úr/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/upernet/configuration_upernet.pyr   ÚUperNetConfig.__post_init__K   sY   ø€ Ü'Lñ (
Ø ×0Ñ0ð(
à (ð(
ð Ò Hð#ð(
ð ñ(
Ñ$ˆÔ˜fô 	‰ÒÑ' Ô'ó    c                óþ   <€ V ^8„  d   Qh/ S[ S[,          R,          ;R&   S[;R&   S[;R&   S[S[,          S[S[R3,          ,          ;R&   S[;R&   S[;R&   S[R,          ;R	&   S[;R
&   S[;R&   S[;R&   S[;R&   # )é   Nr   Úhidden_sizeÚinitializer_range.Úpool_scalesÚuse_auxiliary_headÚauxiliary_loss_weightÚauxiliary_in_channelsÚauxiliary_channelsÚauxiliary_num_convsÚauxiliary_concat_inputÚloss_ignore_index)Údictr   ÚintÚfloatÚlistÚtupleÚbool)ÚformatÚ__classdict__s   "€r   Ú__annotate__ÚUperNetConfig.__annotate__   sÊ   ø‡ ‚ ñN Ñ,Õ,¨tÕ3Ñ:ñO ñP ÑñQ ñR Ñ#ñS ñT ‘c•™U¡3¨ 8�_Õ,Ñ;ñU ñV Ñ#ñW ñX !Ñ&ñY ñZ  �:Ñ,ñ[ ñ\ Ñ!ñ] ñ^ Ñ ñ_ ñ` !Ñ(ña ñb Ñ òc r   )r   )é   r    é   é   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer   Úsub_configsr   r!   r"   r#   r$   r%   r&   r'   r(   r)   r*   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r   r2   s   @@r   r	   r	      sh   ù‡ € ñðB €JØ$ jÐ1€Kà6:€OØ€KØ#ÐØ/;€KØ#ÐØ#&ÐØ(,ÐØ!ÐØ ÐØ#(ÐØ Ðõ	(÷g … r   r	   N)r<   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   Úauto.configuration_autor   r	   Ú__all__r   r   r   Ú<module>rI      sN   ðñ "å .å CÝ 3Ý #Ý 0ñ Ð<Ô=Øô:(Ð$ó :(ó ó >ð:(ðz Ð
‚r   